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- W2149940198 abstract "In this paper, we apply a context-sensitive technique for multimodal emotion recognition based on feature-level fusion of acoustic and visual cues. We use bidirectional Long ShortTerm Memory (BLSTM) networks which, unlike most other emotion recognition approaches, exploit long-range contextual information for modeling the evolution of emotion within a conversation. We focus on recognizing dimensional emotional labels, which enables us to classify both prototypical and nonprototypical emotional expressions contained in a large audiovisual database. Subject-independent experiments on various classification tasks reveal that the BLSTM network approach generally prevails over standard classification techniques such as Hidden Markov Models or Support Vector Machines, and achieves F1-measures of the order of 72 %, 65 %, and 55 % for the discrimination of three clusters in emotional space and the distinction between three levels of valence and activation, respectively." @default.
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- W2149940198 date "2010-09-26" @default.
- W2149940198 modified "2023-10-17" @default.
- W2149940198 title "Context-sensitive multimodal emotion recognition from speech and facial expression using bidirectional LSTM modeling" @default.
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- W2149940198 doi "https://doi.org/10.21437/interspeech.2010-646" @default.
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